Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

Optical Label-Based Cost-Effective DWDM Optical Network Performance Monitoring Using Low-Bandwidth PD with Novel SRS Mitigation DSP

Large-scale dense wavelength division multiplexing(DWDM)multi-channel performance monitoring is one of the indispensable technologies for the flexible optical networks.The existing Labelbased monitoring scheme requires expensive optical demultiplexing components/equipment to avoid the influence of stimulated Raman scattering(SRS),which is not only costly and bulky,but also could not monitor the wavelength channels simultaneously.In this paper,a low-cost,high-accuracy monitoring scheme based on Optical Label Method is proposed for DWDM networks,where the optical channel power and node identification(ID),as the main monitoring targets that both can indicate or evaluate the channel connection status,could be efficiently monitored.In the scheme,a novel digital signal processing(DSP)method of SRS mitigation is proposed and demonstrated,and an asynchronous code-division multiple access(A-CDMA)based digital label encoding and decoding method is adopted to distinguish the node ID so that channel initial added node can be accurately verified,thereby wavelength connection status can be reliably monitored by combining the channel power and node ID information.The simulation results show that each wavelength channel power and node ID can be accurately monitored only by low bandwidth photoelectric detector(PD)under the condition of 80 wavelengths and 10 spans at C-band.

Control performance monitoring and advanced control towards energy conservation

High performance control of an interactive process such as iron and steel plant relies on ability to honor safety and operational constraints;reduce the standard deviations of variables that need to be controlled(e.g.product quantity,quality );de-bottlenecking the process;and,maximize profitability or lower cost(e.g.energy savings, improve hot metal content).These objectives may be prioritized in this order,but can vary and are very difficult to achieve optimally through conventional control.A multivariable predictive controller solution,along with its extensive inferential sensor and built-in optimizer,provides online closed loop control and optimization for many interactive metal and mining processes to lower the energy cost,increase throughput,and optimize product quality and yield. Control loop performance is also a key factor to improve iron and steel plant automation and operation result; Honeywell CPM offers vender-independent product which provides monitoring,tuning,modeling of control loop and sustainable loop performance analysis and maintenance solution towards operation stability and energy saving.

Performance Monitoring in Transparent Reconfigurable WDM Networks

This paper classifies and surveys different approaches proposed for performance monitoring, in particular the optical signal-to-noise ratio (OSNR) monitoring, in transparent reconfigurable WDM networks. Some considerations for future monitoring schemes are discussed.

Novel anomaly detection approach for telecommunication network proactive performance monitoring

The mode of telecommunication network management is changing from“network oriented”to“subscriber oriented”.Aimed at enhancing subscribers’feeling,proactive performance monitoring(PPM)can enable a fast fault correction by detecting anomalies designating performance degradation.In this paper,a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average(ARIMA).Furthermore,under the assumption that the training residual is a white noise process following a normal distribution,the associated confidence interval of prediction can be figured out under any given confidence degree 1–αby constructing random variables satisfying t distribution.Experimental results verify the method’s effectiveness.

Multiple-impairment monitoring for optical duobinary system based on delay-tap asynchronous sampling

A technique using artificial neural networks trained with parameters derived from delay tap plots for optical performance monitoring in 40 Gbit/s duobinary system is demonstrated. Firstly, the optical signal is delay tap sampled to obtain two-dimensional histogram, known as delay tap plots. Secondly, the features of delay tap plots are extracted to train the feed forward, three-layer preceptor structure artificial neural networks. Finally, the outputs of trained neural network are used to monitor optical duobinary signal impairments. Simulation of optical signal noise ratio （ OSNR）, chromatic dispersion （CD）, and differential group delay （DGD） monitoring in 40 Gbit/s optical duo- binary system is presented. The proposed monitoring scheme can accurately identify simultaneous impairments without requiring synchronous sampling or data clock recovery. The proposed technique is simple, cost-effective and suitable for in-service distributed OPM.

In-depth analysis of traffic congestion using computational fluid dynamics (CFD) modeling method

This paper introduces computational fluid used in aerospace engineering, to deal with surface physical and mathematical foundations of CFD, this traffic problems such as queue/platoon distribution, dynamics （CFD）, a numerical traffic flow related problems. approach widely and successfully After a brief introduction of the paper develops CFD implementation methodology for modeling shockwave propagation, and prediction of system performance. Some theoretical and practical applications are discussed in this paper to illustrate the implementation methodology. It is found that CFD approach can facilitate a superior insight into the formation and propagation of congestion, thereby supporting more effective methods to alleviate congestion. In addition, CFD approach is found capable of assessing freeway system performance using less ITS detectors, and enhancing the coverage and reliability of a traffic detection system.

通过运行数据的性能分析评估Mewis导流罩的效率

Τhe efficiency of a Mewis propeller duct by the analysis of ship operational data is examined.The analysis employs data collected with high frequency for a three-year period for two siter vessels,one of them fitted with a Mewis type duct.Our approach to the problem of identifying improvements in the operational performance of the ship equipped with the duct is two-fold.Firstly,we proceed with the calculation of appropriate Key Performance Indicators to monitor vessels performance in time for different operational periods and loading conditions.An extensive pre-processing stage is necessary to prepare a dataset free from datapoints that could impair the analysis,such as outliers,as well as the appropriate preparations for a meaningful KPI calculation.The second approach concerns the development of multiple linear regression problem for the prediction of main engine fuel oil consumption based on operational and weather parameters,such as ship’s speed,mean draft,trim,rudder angle and the wind speed.The aim is to quantify reductions due to the Mewis duct for several scenarios.Key results of the studies reveal a contribution of the Mewis duct mainly in laden condition,for lower speed range and in the long-term period after dry-docking.

OWDP and Its Secure Implementation

Here we present one design based on OWDP for secure high-speed IP network performance monitor system. Based on the analysis of OWDP protocol and the high-speed IP network performance's real-time monitor infrastructure, the paper illustrates the potential security problems in OWDP and its possible weakness when applied in the monitor infrastructure. One secure improvement design based on Otway-Rees authentication protocol is put forward, which can improve the security of the implementation of OWDP and the monitor architecture. Having kept OWDP's simplicity and efficiency, the design satisfies the real-time demand of high-speed network performance monitor and will effectively safeguard the monitor procedure against intensive attacks.

Toward Low-Cost Flexible Intelligent OAM in Optical Fiber Communication Networks

Low-cost,flexible and intelligent optical performance monitoring and management is a key enabling technology for network quality guarantee,especially in the era of explosive growth of communication capacity and network scale.However,to the best of our knowledge,it is extremely challenging to implement real-time performance monitoring and operations,administration and maintenance(OAM) in a highly complex dynamic network.In this paper,we propose an innovative optical identification(OID) scheme that can realize both performance monitoring and some advanced OAM sub-functions.The basic concepts,applications,challenges and evolution directions of this OID tool are also discussed.

Reliability Management for Information System

An integrated intelligent management is presented to help organizations manage many heterogeneous resources in their information system. A general architecture of management for information system reliability is proposed, and the architecture from two aspects, process model and hierarchical model, described. Data mining techniques are used in data analysis. A data analysis system applicable to real-time data analysis is developed by improved data mining on the critical processes. The framework of the integrated management for information system reliability based on real-time data mining is illustrated, and the development of integrated and intelligent management of information system discussed.

Unified control and detection framework and its applications: a review, some new results, and future perspectives

Initiated three decades ago,integrated design of controllers and fault detectors has continuously attracted research attention.The recent development of the unified control and detection framework with an observer-based residual generator in its core gives a more general form of the previous works.Its applications to residual centred modelling of uncertain control systems,fault detection in feedback control systems with uncertainties,fault-tolerant control(FTC)as well as control performance degradation monitoring,detection and recovery are introduced.In conclusion,some future perspectives are proposed.

Chromatic dispersion monitoring using semiconductor optical amplifier

An all-optical real-time chromatic dispersion （CD） monitoring technique is proposed and demonstrated for 40Gbit/s differential phase-shifts keying （DPSK） signal, utilizing the cross modulation effects of semicon- ductor optical amplifier （SOA）. The optical power of the output spectral components, which is from the probe＇s frequency up to the signal bandwidth, is used for CD monitoring. This technique provides a wide monitoring range with large variation scale. The impacts of the polarization mode dispersion （PMD） and the optical signal-to-noise ratio （OSNR） on the CD monitoring results are theoretically analyzed and then experimentally investigated, showing that they have slight influence on the monitoring results within a certain range. Furthermore, simulated results for quadrature phase shift keying （QPSK） signal at 80 Gbit/s are also demonstrated, indicating that this technique is suitable for advanced modulated format as well.

PMU Guided Structure Data-Layout Optimization

Existing methods of obtaining runtime feedback for structure data-layout optimization have several drawbacks, such as large overhead and difficulty composing training sets. As a result, structure data-layout optimization is not widely used. To overcome these drawbacks, a performance monitoring unit （PMU） sampling method was developed with much less overhead and better portability and usability. An algorithm was developed to correct incomplete and inaccurate PMU sampling. With the corrected PMU feedback, a structure data-layout optimizer achieved a 45.1% performance improvement compared to a design without data-layout optimization, which is 97.6% of the performance improvement achieved with instrumented feedback. Calculation of the PMU feedback increased the execution time by 12.3%, compared to the overhead for the instrumented feedback of 341.5%. Tests show that the PMU feedback is efficient and effective for structure data-layout optimization.

An Online Visualization System for Streaming Log Data of Computing Clusters

Monitoring a computing cluster requires collecting and understanding log data generated at the core, computer, and cluster levels at run time. Visualizing the log data of a computing cluster is a challenging problem due to the complexity of the underlying dataset： it is streaming, hierarchical, heterogeneous, and multi-sourced. This paper presents an integrated visualization system that employs a two-stage streaming process mode. Prior to the visual display of the multi-sourced information, the data generated from the clusters is gathered, cleaned, and modeled within a data processor. The visualization supported by a visual computing processor consists of a set of multivariate and time variant visualization techniques, including time sequence chart, treemap, and parallel coordinates. Novel techniques to illustrate the time tendency and abnormal status are also introduced. We demonstrate the effectiveness and scalability of the proposed system framework on a commodity cloud-computing platform.

Performability analysis of avionics system with multilayer HM/FM using stochastic Petri nets

The integrated modular avionics （IMA） architecture is an open standard in avionics industry, in which the number of functionalities implemented by software is greater than ever before. In the IMA architecture, the reliability of the avionics system is highly affected by the software applications. In order to enhance the fault tolerance feature with regard to software application failures, many industrial standards propose a layered health monitoring/fault management （HM/FM） scheme to periodically check the health status of software application processes and recover the malfunctioning software process whenever an error is located. In this paper, we make an analytical study of the HM/FM system for avionics application software. We use the stochastic Petri nets （SPN） to build a formal model of each component and present a method to combine the components together to form a complete system model with respect to three interlayer query strategies. We further investigate the effectiveness of these strategies in an illustrative system.